Expect more from your RNA analyses with the new SOPHiA DDM RNAtarget Technology

Published on 04/20/2022

11 min read

Q&A with the Clinical Application Product Manager, Mikhail Pertziger, PhD

Several RNA alterations have been described in Oncology, including gene fusions, recognized driver mutations in neoplasia1. More than 10,000 gene fusions have already been identified in human cancers1; it is estimated that up to 80% of solid tumors could benefit from gene fusion testing2. The number of new drugs specifically targeting gene fusion-positive cancers keeps growing: the advantages that proper gene fusion detection could bring to clinical cancer management are noticeable.

Transcriptome sequencing has emerged as an effective method to identify gene fusions and has become a routine task in cancer research and precision medicine3. However, although a variety of computational tools have been developed over the years, an optimal solution with high analytical performance for fusion detection and the ability to maximize the insights from small precious RNA samples has been lacking.

SOPHiA DDM RNAtarget Technology addresses these requirements by combining powerful novel (partner-agnostic) fusion detection capabilities as well as SNV/Indels detection in selected genes and expression changes assessment. Powered by a deep learning algorithm, the Technology works with a very low sample input, a fully customizable gene panel, and a streamlined automated workflow that supports all stages of the analysis with high sensitivity. Finally, a convenient yet powerful and fast results visualization and interpretation are ensured by the associated SOPHiA DDM™ Platform.

To better understand how SOPHiA GENETICS developed the SOPHiA DDM RNAtarget Technology and its features, we sat down with Mikhail Pertziger, the Clinical Application Product Manager for SOPHiA DDM RNAtarget Technology at SOPHiA GENETICS.

1. Tell us about yourself and what motivated you to work on the Technology to detect gene fusions in cancer research using RNA sequencing data.

After studying Biomedical Science for my undergraduate degree, I continued with a PhD in the Molecular Biology of Breast and Colon Cancers, so Cancer and Molecular Diagnostics is very much the area where I have spent a lot of my academic and industry years. They say that the 21st century is the era of biology, and I will have to add that precision medicine is the future of cancer management. Biomarker-guided therapies are introducing dramatic differences in how oncology conditions are managed. Fusions are the latest frontier to receive broad applicability in the clinic with more and more drugs being introduced – this is bound to grow and accelerate. I’m excited to be working on a product that allows our customers to have access to a technology that takes on the learnings of the previous years and enables them to be more confident about detecting fusions.

I have worked at SOPHIA GENETICS for just over three years and currently lead the SOPHiA DDM RNAtarget Technology development and launch. The development team includes experts in a broad range of applications, including the core development of BioInformatics, NGS, programming, as well as logistics, Regulatory, Legal, and Marketing. 

2. Why is the detection of gene fusions from RNA sequencing data important in oncology?

As I mentioned previously, gene fusions are the latest type of biomarker to receive broad applicability in cancer management. The results of targeting fusions reported in clinical trials and now being seen in routine care are fascinating. The number of new drug approvals in fusion-positive cancers has been continuously increasing over the last decade – up to 80% of solid tumors NGS tests could benefit from the inclusion of fusion testing. With histology-agnostic approvals, this number approaches 100%2. In parallel, more clinical trials are being rolled out to target fusion-positive cancers, hopefully leading to further improvements in treatment options in the near future.

Up to 80% of solid tumors NGS tests could benefit from the inclusion of fusion testing2

3. How did you develop the SOPHiA DDM RNAtarget Technology, and what major features are used in this application?

SOPHiA DDM RNAtarget Technology came about from our users’ feedback on the need to have an application that allows them to detect novel fusions without sacrificing sensitivity in smaller biopsy samples. The work on this Technology started more than a year ago and has involved many feasibility and optimization studies to ensure that we’re not just providing a regular solution, but a product that really helps users achieve more.

What we wanted to provide with this product was the ability for users to have a high-performance fusion detection that could be run in a very small amount of material, a streamlined (but robust) workflow, as well as the ability to not only detect fusions but also be able to extract as much information as possible from that small sample with detection of SNVs and Expression changes. For convenience, the gene content can be customized to fit the lab’s individual needs, automated workflow to reduce resource constraints, and, finally, the product runs on the industry-leading SOPHiA DDM™ Platform, providing convenient visualization, annotation, and reporting of the results.

4. Can you elaborate on SNV detection in RNA samples?

SNV detection in RNA is an intriguing area of development. There are several applications where SNV detection is beneficial, including the ability to run an RNA-only workflow in cases where genes of interest are sufficiently highly expressed. SNV detection in RNA also opens up the possibility of running RNA and DNA workflow sequentially, where the initial RNA workflow will likely detect the majority of relevant variants, leaving only a subset of samples that need to be also processed through the DNA workflow.

Another benefit of detecting SNVs in RNA is the ability to use them as an additional data point in calling SNVs in DNA or using RNA-based SNVs as a backup in case of issues with DNA.

Finally, having the information on SNV VF% in RNA, is like adding an additional dimension to the molecular profile created by DNA SNVs – as this provides more dynamic, rich, and potentially more insightful information on the state of things in the tumor.

Overall, there are many novel and unique ways this feature of SOPHiA DDM RNAtarget Technology could be used, and I’m excited to see how our users will utilize it.

5. How can this help cancer research to hopefully impact the oncology patient’s journey in the future?

One of the primary applications for the product is, of course, Lung cancer because of the limited amount of biopsy material that is generally available and a high number of clinically relevant fusions in this pathology. At the same time, the solution can be deployed to test any solid tumor, and we’re looking at the possibility of running it in blood tumors as well. Moreover, because the gene content is entirely customizable, users can tailor the gene content to the needs of their labs, clinical research, or clinical trials that they want to be part of. This makes the application very versatile while removing the obstacle of manually optimizing the pipeline’s performance because the SOPHiA BioIT team will take care of that. Given the proliferation of tumor-agnostic biomarkers and, in particular, tumor agnostic fusions, the applicability of this Technology is only going to grow and expand in the future, and the fusion detection would become an integral part of genomic profiling of any cancer, together with SNV and CNVs.

SOPHiA DDM RNAtarget Technology can be deployed to test any solid tumor

6. Compared to current approaches for fusion detection, what makes SOPHiA DDM RNAtarget Technology exciting and different? Are there any specific methods for fusion detection that the Technology uses that these other solutions don’t?

It’s really the combination of 3 main features that make for a cohesive and well-rounded product that offers a lot of value from several perspectives – Novel fusion detection, High sensitivity at low input amounts, and Customizability of the panel. These features provide an excellent foundation for a future-proof, high-performance solution. If you look at the market, there are very functional solutions that offer one or two of these features, but not all 3.

In addition to those three features, we included other functionality that I refer to as “two data points for every variant type,” where 5’-3′ imbalance serves as an additional data point for calling fusions, SNV detection in RNA can be used together with SNV calls in DNA, and expression changes provide further details and reassurance for the CNVs.

Finally, the streamlined protocol that can also be automated further refines the convenience factor of this solution.

7. How is RNAtarget technology able to detect/identify new/novel fusions?

“How” is straightforward: SOPHiA DDM RNAtarget Technology utilizes a hybrid-capture approach, which targets the key clinically relevant kinases. This protocol is augmented by a careful probe design process to make sure the panel performs at the highest level in the hands of our users.

On the other hand, it is worth highlighting that the detection of novel (or partner-agnostic) fusions is becoming a more and more prominent feature requested by labs striving to provide the highest level of care. This is underpinned by the higher inherent sensitivity, which is becoming more important in the rise in approval of fusion targeting therapies in a partner agnostic manner.

8. Finally, if you were asked to summarize in one sentence the most important challenges that users can overcome with SOPHiA DDM RNAtarget Technology?

I would say it comes down to the challenges of needing to have high sensitivity in low sample input, the ability to detect novel fusions, and having a tailored solution that perfectly fits the needs of the lab, plus addressing the need for a convenient yet powerful visualization and interpretation platform – the SOPHiA DDM™ Platform.

References

  1. Mertens F, Johansson B, Fioretos T, Mitelman F. Nat Rev Cancer. 2015; 15(6):371-381.
  2. Mosele F. Ann Oncol. 2020; Nov;31(11):1491-1505.
  3. Schram AM, Chang MT, Jonsson P, Drilon A. Nat Rev Clin Oncol. 2017; 14: 735–748.
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